GANs suffer from a variety of issues such as training instability and mode collapse that require careful setting of hyperparameters for successful training.  [1]} suggests WGAN-GP training loss which can mitigate the issues caused by insufficient overlap between generated and target distributions leading to an increased stability of training. Karnewar et al. [2]} builds on the previous work  [3]} and proposes the Multi-Scale Gradients (MSG) method where images are produced in exponentially growing resolutions starting from the smallest size of \(4 \times 4\)  till the final output size allowing the discriminator to provide feedback in all resolutions instead of just one which further mitigates these issues.
